Zachary Depp, Halit Bugra Tulay, C. Emre Koksal (The Ohio State University)

The traditional vehicular roll-jam attack is an effective means to gain access to the target vehicle by jamming and recording key fob inputs from a victim. However, it requires specific knowledge of the attack surface, and delicate tuning of software-defined radio parameters. We have developed an enhanced version of the roll-jam attack that uses a known noise signal for jamming, in contrast to the additive white Gaussian noise that is typically used in the attack. Using a known noise signal allows for less strict tuning of the software-defined radios used in the attack, and allows for digital noise removal of the recorded input to enhance the replay attack.

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WIP: Modeling and Detecting Falsified Vehicle Trajectories Under Data...

Jun Ying, Yiheng Feng (Purdue University), Qi Alfred Chen (University of California, Irvine), Z. Morley Mao (University of Michigan and Google)

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InfoMasker: Preventing Eavesdropping Using Phoneme-Based Noise

Peng Huang (Zhejiang University), Yao Wei (Zhejiang University), Peng Cheng (Zhejiang University), Zhongjie Ba (Zhejiang University), Li Lu (Zhejiang University), Feng Lin (Zhejiang University), Fan Zhang (Zhejiang University), Kui Ren (Zhejiang University)

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Assessing the Impact of Interface Vulnerabilities in Compartmentalized Software

Hugo Lefeuvre (The University of Manchester), Vlad-Andrei Bădoiu (University Politehnica of Bucharest), Yi Chen (Rice University), Felipe Huici (Unikraft.io), Nathan Dautenhahn (Rice University), Pierre Olivier (The University of Manchester)

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